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Hypothetical Estimands in Clinical Trials: A Unification of Causal Inference and Missing Data Methods
The ICH E9 addendum introduces the term intercurrent event to refer to events that happen after treatment initiation and that can either preclude observation of the outcome of interest or affect its interpretation. It proposes five strategies for handling intercurrent events to form an estimand but...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10228513/ https://www.ncbi.nlm.nih.gov/pubmed/37260584 http://dx.doi.org/10.1080/19466315.2022.2081599 |
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author | Olarte Parra, Camila Daniel, Rhian M. Bartlett, Jonathan W. |
author_facet | Olarte Parra, Camila Daniel, Rhian M. Bartlett, Jonathan W. |
author_sort | Olarte Parra, Camila |
collection | PubMed |
description | The ICH E9 addendum introduces the term intercurrent event to refer to events that happen after treatment initiation and that can either preclude observation of the outcome of interest or affect its interpretation. It proposes five strategies for handling intercurrent events to form an estimand but does not suggest statistical methods for estimation. In this article we focus on the hypothetical strategy, where the treatment effect is defined under the hypothetical scenario in which the intercurrent event is prevented. For its estimation, we consider causal inference and missing data methods. We establish that certain “causal inference estimators” are identical to certain “missing data estimators.” These links may help those familiar with one set of methods but not the other. Moreover, using potential outcome notation allows us to state more clearly the assumptions on which missing data methods rely to estimate hypothetical estimands. This helps to indicate whether estimating a hypothetical estimand is reasonable, and what data should be used in the analysis. We show that hypothetical estimands can be estimated by exploiting data after intercurrent event occurrence, which is typically not used. Supplementary materials for this article are available online. |
format | Online Article Text |
id | pubmed-10228513 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-102285132023-05-31 Hypothetical Estimands in Clinical Trials: A Unification of Causal Inference and Missing Data Methods Olarte Parra, Camila Daniel, Rhian M. Bartlett, Jonathan W. Stat Biopharm Res Research Articles The ICH E9 addendum introduces the term intercurrent event to refer to events that happen after treatment initiation and that can either preclude observation of the outcome of interest or affect its interpretation. It proposes five strategies for handling intercurrent events to form an estimand but does not suggest statistical methods for estimation. In this article we focus on the hypothetical strategy, where the treatment effect is defined under the hypothetical scenario in which the intercurrent event is prevented. For its estimation, we consider causal inference and missing data methods. We establish that certain “causal inference estimators” are identical to certain “missing data estimators.” These links may help those familiar with one set of methods but not the other. Moreover, using potential outcome notation allows us to state more clearly the assumptions on which missing data methods rely to estimate hypothetical estimands. This helps to indicate whether estimating a hypothetical estimand is reasonable, and what data should be used in the analysis. We show that hypothetical estimands can be estimated by exploiting data after intercurrent event occurrence, which is typically not used. Supplementary materials for this article are available online. Taylor & Francis 2022-07-06 /pmc/articles/PMC10228513/ /pubmed/37260584 http://dx.doi.org/10.1080/19466315.2022.2081599 Text en © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Olarte Parra, Camila Daniel, Rhian M. Bartlett, Jonathan W. Hypothetical Estimands in Clinical Trials: A Unification of Causal Inference and Missing Data Methods |
title | Hypothetical Estimands in Clinical Trials: A Unification of Causal Inference and Missing Data Methods |
title_full | Hypothetical Estimands in Clinical Trials: A Unification of Causal Inference and Missing Data Methods |
title_fullStr | Hypothetical Estimands in Clinical Trials: A Unification of Causal Inference and Missing Data Methods |
title_full_unstemmed | Hypothetical Estimands in Clinical Trials: A Unification of Causal Inference and Missing Data Methods |
title_short | Hypothetical Estimands in Clinical Trials: A Unification of Causal Inference and Missing Data Methods |
title_sort | hypothetical estimands in clinical trials: a unification of causal inference and missing data methods |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10228513/ https://www.ncbi.nlm.nih.gov/pubmed/37260584 http://dx.doi.org/10.1080/19466315.2022.2081599 |
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